In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.

Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review. Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information. And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context. In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts. It is capable of extracting the syntactic structure features and syntax information of aspect and context. In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model.

Aviation ground safety and protective devices are critical for flight safety; however, their unintentional retention on aircraft after maintenance remains a persistent risk. Existing deep learning-based approaches for aviation safety have predominantly followed a reactive paradigm, detecting FOD on runways or inspecting the aircraft for inadvertently retained tools post-maintenance. In contrast, this paper advocates a proactive philosophy: using a neural network to recognize and inventory all ground safety and protective devices immediately after maintenance closure, thereby preventing retention incidents at their source. However, realizing this proactive verification is technically challenging—object detection for these devices often suffers from loss of fine-grained detail due to downsampling and inherently sparse semantic information of the targets. To this end, we propose an Information Retention and Feature Screening Synergistic Network (RS-Net) grounded in information bottleneck theory. The network comprises a main branch that enhances discriminative features through attention-guided screening, and an auxiliary branch, used only during training, that preserves fine-grained spatial details via information-retentive convolutions. A Dual-State Region Refinement Module (DRM) provides configurable support for both branches, decoupling the conflicting objectives of background compression and detail preservation. Experiments on a self-constructed dataset collected from real airline maintenance operations demonstrate that RS-Net substantially outperforms the strong YOLOv9 baseline, achieving gains of 4.531% in F1-score, 2.533% in mAP_0.5, and 1.429% in mAP_0.5:0.95. Cross-dataset experiments further validate its strong generalization capability.